Maiketing Letters 7:3 (1996) 237-247
CKluwer Academic Publishen, Manufactured in The Netherlands
An Empirical Comparison of Consumer-Based
Measures of Brand Equity
MANOJ K. AGARWAL
School of Management. Binghamton University. Binghamton. NY 13902-6015
VITHALA R. RAO
Johnson Graduate School of Management. Cornell University. Ithaca. NY 14853-4201
(Received 11-29-93 / Accepted 3-21-96)
Key words: Brand Equity, Predictive Validity, Marketing Research Methods, Discrete Choice. Dollar-Metric
Method
Abstract
This article connpares eleven diflerent consumer-based brand equity measures and evaluates their convergence.
Predictive validity al the individual and aggregate levels is also investigated. Measures based on the dollar metric method and discrete choice methodology predict choices extremely well in a simulated shopping environment, as well as purchase-intention and brand-quality scales.
Brand equity has become an important topic in the business world, and its appropriate
measures can address a number of marketing and brand managers' concems. Brandequity measures, at the level of the firm or the brand, can be developed with different
types of data. These dats include financial market value data (Simon and Sullivan, 1993;
Lane and Jacobson, 1?95), historical accounting data (Barwise, Higson, Likierman, and
Marsh, 1991), scanner data (Kamakura and Russell, 1993), and consumer surveys
(Winters, 1991; Park and Srinivasan, 1994).
We focus on consumer-based measures in this article for several reasons. First, consumer-based measures allow the assessment of equity at the brand level. Second, a lot
of published research in marketing uses these types of measures. Third, marketing managers are highly familiar with consumer-based measures that use data commonly collected in research studies and will thus Tind them easy to use and understand.
While a wide variety of measures and approaches have been used, there has been little attempt to link the various measures of brand equity or to examine their convergent
validity. In this article, we examine eleven different consumer-survey-based measures
of brand equity, developed at both aggregate and individual respondent levels. We
explore the convergence of these measures (in the same spirit as Kumar, Ghosh, and
Tellis, 1982, did for repeat buying) and validate them against laboratory data of actual
purchases.
238
MANOJ K. AGARWAL AND VrrHALA R. RAO
1. Brand equity
Aaker (1991) and Keller (1993) have both provided conceptual schemes that link brand
equity with various consumer response variables, SpeciHcally, Aaker (1991) lists four
major consumer-related bases of brand equity: brand loyalty, name awareness, perceived
quality, and other brand associations. The stronger these bases are. the higher is the resulting brand equity. Keller (1993) presents a knowledge-based framework for creating brand
equity. This knowledge is composed of two major dimensions: brand awareness and brand
image. Awareness is composed of brand recall and recognition, while image is composed
of various associations of the brands.
Both these authors suggest a variety of indirect measures and methods to estimate brand
equity based on theirframeworks.For example, Aaker (1991) suggests using repurchase
rates, switching costs, level of satisfaction, preference for brand, and perceived quality on
various product and service dimensions as potential measures, among others. Likewise,
Keller (1993) suggests correct top-of-mind recall, free associations, ratings of evaluations,
and beliefs of associations as some of the measures of brand knowledge.
We differentiate between the direct and indirect approaches to measuring brand equity
(Aaker, 1991; Keller, 1993). The direct approach tries to assess the added value of the
brand and appears to be the accepted definition of brand equity (Farquhar. 1989; Keller,
1993). The indirect approach tries to identify the potential sources of brand equality. An
understanding of these sources for a rirm's own and competitive brands is critical for the
brand manager (Keller, 1993; Parker and Srinivasan, 1994). We focus mostly on the indirect measures.
Besides the measures suggested by academic researchers, various marketing research
firms use their own proprietary measures of brand equity. Winters (1991) reports at least
six such approaches—the measurement of share of mind and esteem (Landor Associates),
perception of quality (Total Research Corporation), willingness to continue to purchase a
brand (Market Facts, Inc.), level of commitment to a brand (Yankelovich Clancy
Shulman), profit potential (Longman-Moran Analytics), and a composite of awareness,
liking, and perceived quality (DDB Needham Worldwide), Since these mea.sures assess
the underlying constructs of awareness, perceptions, preferences, and intentions and do
not gauge the added value of brands, they should be considered indirect.
2. Measures of brand equity
In order to integrate varied measures of brand equity, we use a framework based on the
perceplion-preference-choice paradigm and the hierarchy of effects model (Lavidge and
Steiner, 1961; McGuire, 1972), This framework provides measures linked to the stages
through which a consumer passes and can thus provide useful diagnostic information to
the manager. We selected eleven measures that relate to the different stages of the hierarchy. These measures are operationaiized both at the individual and aggregate levels,
• Awareness: Awareness is measured by unaided recall and familiarity. Recall (labeled
Ml) is measured at the individual level by whether the brand is recalled or not without
COMPARISON OF CONSUMER-BASED MEASURES OF BRAND EQUITY
239
the use of any aid (coded as 0 or 1) and at the aggregate level by the percentage of consumers recalling the brand. Familiarity (M2) of an individual with a brand is measured
on a six-point scale with categories such as "heard o f it," "heard of but never used it,"
"heard of and used it," "presently using," and so on,
• Perceptions and attitudes: We use three different measures, labeled M3 to M5, in this study.
A composite multiattribute weighted score (M3) is estimated by the sum often attribute ratings multiplied by their respective importances. Two single-item scales, a six-point value of
money (M4) and a seven-point quality of brand name (M5) scale are also used.
• Preferences: We operationaiized explicit preference on a six-point poor-excellent scale
for the overall evaluation of brand (M6). Implicit preference was measured utilizing the
price premium for switching between brands and operationaiized by using the dollar
metric scaling method (Pessemier, Burger, Teach, and Tigert, 1971). Information is
obtained on which brand is preferred for each pair of brands, as well as on the price premium at which the preference changes. We propose M7, an aggregate-level-only measure that uses pairwise preference dala without the price premium information. The
number of preferred brands in each pair is cumulated at the sample level, and a dummy
variable logit regression is performed. The coefficient for each brand dummy is used as
measure M7. M8 uses the price premium information, wherein the dependent variable
is the price difference in each pair at which preference switches. Dummy variable logit
regressions are performed both at the individual and aggregate levels, and the brand
dummy coefficients are used as equity measures.
• Choice intentions: We use a single-item measure, M9, based on an explicit question about
likelihood of buying each brand on a 0 to lOO-point scale, similar to one used in this context by Aaker and Keller (1990), As an alternate implicit-intention measure, we used the
discrete-choice methodology (Louviere and Woodworth, 1983), On the basis of a factorial design, thirty-two choice sets, with each of thirteen brands being either present or absent
in the choice set, were shown to the respondents. Respondents were asked to choose one
of the available brands (or none) in each choice set. We used the brand-specific coefficients from these data to measure brand equity; these constitute our measure MIO, This
measure is estimated at the individual level using MNL regression for each respondent and
at the aggregate level by using an aggregate MNL regression (Greene, 1989) procedure,
• Actual choice: We used a self-reported past-purchase rate (converted to an equivalent
buying rate per year) as measure M i l , Actual purchases in an experimental lab context
are also recorded and used fbr validation,
• Research questions: While there is no prior research to formulate hypotheses among the
various measures of brand equity, we focused on the following research questions: (1)
How are various customer-based measures of brand equity related to each other? (2)
How well do the measures predict lab choice at the individual level? (3) How well do
the measures predict market shares at the aggregate level?
3. Empirical study
We conducted a study in several phases among 114 undergraduate students in a required
marketing course at a large university over a period of four months. The average age of the
240
MANOJ K. AGARWAL AND VlTHAl.A R. RAO
respondents was twenty-one years, and 74.6 percent of them were female. Based on an
exploratory study of product categories that are frequently purchased by students, we
selected candy bars for this empirical examination.
3.1. Brands
We chose an initial set of twenty-two brands from a list of about eighty nationally sold
brands, compiled from Simmons Market Research Bureau and the campus best sellers.
After eliminating nine brands that the majority of respondents had never heard of, the final
set of thirteen brands selected for further study represented brands marketed by three
major manufacturers of candy bars—namely. Mars (3 Musketeers, M&M's Plain, M&M's
Peanut, Mars Almond Bar, Milky Way, Snickers, and Twix), Hershey (Hershey's Almonds,
Kit Kat, and Reese's Peanut Butter Cups), and Nestle (Baby Ruth, Butterfmger, and Nestle
Crunch).
3.2. Attributes
An initial list of eighteen potentially important attributes was developed based on discussions with experienced market researchers in candy manufacturing firms, in-depth interviews, as well as minifocus groups with students. Further, a pretest sample of thirty students rated the importance of these attributes in the formation of their overall preference
for candy bars. These data were factor analyzed to select attributes for final survey. This
process yielded ten attributes: overall taste, chocolaty taste, chocolaty experience, peanuty taste, peanuty experience, sweetness, chewiness, texture, filling, and quality of ingredients.
3.3. Data
Data were collected via a series of questionnaires (labeled Ql to Q6) spread out through
the study period. The main items in the data collected were recall (Ql), familiarity and frequency of buying (Q2), attribute ratings, attribute importance, and likelihood of purchase
(Q3), likelihood of buying each brand in thirty-two choice sets (Q4), price premiums for
seventy-eight brand pairs (Q5), and perceived quality (Q6).
In the first questionnaire, students were asked to list the brands of candy bars that came
to their mind in the order recalled. On an average, ten brands were recalled with a standard deviation of 3.7; the modal response was ten, while the range was between two and
eighteen brands. Other questionnaires are quite self-explanatory.
Toward the end of the study period, subjects were asked to "purchase" one of the brands
on two successive occasions. For this simulated purchase experiment, the set of thirteen
brands was divided in two subsets of six and seven; the brands in the two subsets were laid
out on a table and offered for "sale." Every subject was allowed to pick one brand from
COMPARISON OF CONSUMER-BASED MEASURES OF BRAND EQUITY
241
each subset, and these candy bars were given to them free. We computed eleven measures
at the aggregate level and ten measures (all except M7) for each individual respondent.
Except for Ml, M7, M8, and MIO, the aggregate measures were averages of the individual measures. The aggregate level estimation method for these four measures has been discussed earlier.
4. Results
In addition to descriptive results, we discuss the degree of correspondence of the measures, as well as their validation gainst actual lab purchases.
4.1. Aggregate level results
Brands differed in their strength on any given measure, and there was no unanimity across
the measures as to which brands were the strongest. The Snickers brand had the highest
scores on six out of the eleven measures and ranked close to the top on the other five measures. Conversely, Mars Almond Bar was the lowest rated on seven of the eleven measures
and was near the bottom on the rest (except unaided recall). Other brands had more mixed
results.
In order to judge consistency, pairwise correlations of the eleven measures. Ml through
Mil, were estimated and are shown in Table 1, Except for the measure Ml (percent unaided recall), all of the measures are highly congruent with each other. Correlations between
the pairs among M2 through Ml 1 range from a low of 0.75 (for familiarity index, M2, and
weighted attribute score, M3) to a high of 0.98 (for derived brand index, M7 and purchase
intention, M9). The correlations between Ml and other measures M2 through Mil vary
between a low of 0.11 to a high of 0.33. In other words, except for top-of-mind awareness,
the remaining aggregate measures of brand equity all seem to be highly consistent.
A factor analysis of these eleven measures revealed two factors explaining 91 percent of
the total variance. Unaided recall (Ml) constituted one factor, while the second factor had
high loadings of all the other measures. Thus it appears that there is a high amount of convergent validity among the measures M2 through Mil,
Table 2 reports lab market shares obtained when the two sets of brands were offered for
"sale" (at no cost) to each respondent. In Set 1, Kit Kat, Nestle, and Reese's were each
chosen by about 20 percent of the sample. In Set 2, the overwhelming choice was for
Snickers and Twix, accounting for two-thirds of the respondents.
Table 3 reports the correlations of the measures with the lab market shares. In Set 1,
only some measures are highly correlated. M2 (familiarity index) and M5 (quality of
brand name) have a negative correlation, while M7 (derived brand index) and M8 (dollar
metric measure) correlate highly in the predicted direction. The correlation of M2 (familiarity index) is close to zero, although negative. M5 (quality of brand name) is negatively
related to market share due to the fact that while the quality ratings were the highest of
M&M brands, they had the lowest market share.
242
MANOJ K. AGARWAL AND VITHALA R. RAO
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COMPARISON OF CONSUMER-BASED MEASURES OF BRAND EQUITY
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Table 2. Lab market shares in the two sets.
Set 1
Brands
Set 2
Percentage of market share
Hershey
KitKal
M&M Plain
M&M Peanuts
Nestle
Reese's Peanut Butter Cup
Total
Brands
Percentage of market share
11.40%
22.81
9.65
12.28
20.18
23.68
3 Musketeers
Baby Ruth
Buncrfinger
Mars Almond Bar
Milky Way
Snickers
Twix
6.14%
8.77
7.90
8.77
4.39
35.09
28.95
IOO
Total
IOO
In Set 2, the measures appear to be correlated with lab maiket shares much better. One of
the reasons for this appears to be that Set 2 includes Mars Almond Bars (rated the lowest on
almost all the measures) and Snickers and Twix (which were rated the highest on all the measures). Thus, Set 1 has brands that fall in the middle of the overall preferences, while Set 2
has brands that were at the extremes. Since there was agreement on almost all the measures
with respect to the lowest and highest preferred brands, the measures do better in Set 2.
Inspecting the average results, M7, M8, and MIO do well in both sets and are more highly correlated with market shares compared to the other measures. Recall that both M7 and
M8 are derived from the price premium data, while MIO is from the discrete-choice data.
4.2. Individual level results
In order to judge the consistency at the individual level, the ten measures were correlated
for each individual respondent. Except for Ml (percentage of unaided recall), all of the
measures appeared to be highly congruent with each other. The average correlations (Table
1) between Ml and other measures M2 through M6 and M8 through Ml 1 varied between
a low of 0.116 (for M1 and M10) to a high of 0.223 (for M1 and M5). For a fair number
of individuals, the correlation between Ml and any of M2 through Ml 1 was negative.
rable } . Correlations between lab market shares and equity measures at the aggregate level.
Measures
Set 1
Set 2
Average
Rank order
M1
M2
Percentage unaided recall
Familiarity index
.00
.26
.80
.13
m
M3
M4
M5
M6
M7
M8
M9
MIO
M11
Weighted attribute score
Value for money
Quality of brand name
Overall evaluation
Brand mdex
Dollar metric measure
Purchase intention
Brand-spcciflccoefTicient
Index of past purchase
.40
.67
.87
.79
10
9
7
.98
.47
.89
.92
.89
.89
.91
.55
.79
.11
.90
.91
.90
.94
.36
.63
.73
-.04
.68
.91
.90
.72
.84
.52
4
11
6
1
2
5
3
8
244
MANOJ K. AGARWAL AND VITHALA R. RAO
The average correlations between the pairs M2 through M11 ranged from a low of
0.444 (for M4 and M5) to a high of 0.803 for the most congruent pair M6 (overall evaluation) and M9 (purchase intention). Further, the correlation between any pair of measures in this set of nine measures (excluding M1) was negative in a very small number
of cases. In other words, except for top-of-mind awareness, the remaining measures of
brand equity all seemed to be convergent. This result is similar to that at the aggregate
level.
The prediction accuracy of measures was evaluated by computing the percentage of the
times the brand that has the highest value according to a measure is chosen by the respondent. Although a variety of choice rules can be used to predict choice at the individual
level, we chose the simple maximum utility rule (Green and Krieger, 1988). In Table 4, we
report the percentage of correct predictions (that is, the brand chosen in the lab experiment) in the two sets. Similar to the aggregate results, prediction accuracy is higher in the
second set than the Tirst. The measures M8 (dollar metric measure) and MIO (brand-specific coefficients) yield the highest average accuracy.
In order to judge the relative importance of the measures in predicting choice, multinomial logit regression analysis was performed (Swait and Louviere, 1993). The dependent
variable was the brand choice in each of the two subsets, and the ten individual level measures were used as the independent variables. Table S shows the results. The fit is very
good in both sets, with rho squared being 0.49 and 0.69. While M5 and M8 are significant
in the first set, M8, M9, and MIO are significant in the second set.
4.3. Summary of results
I. It appears that for this product category, the various measures, except for recall, are
highly consistent with each other. Thus, the indirect measures of brand equity relating
to either perceptions, attitudes, preferences, or choice intentions are convergent.
Table 4. Percent accurate predictions of lab ehoiees for eaeh measure at the individual level.
Percentage of correct
Tirst-rank choices*
Measure
Description
Set 1
Ml
Percentage of unaided recall
Familiarity index
M2
M3
M4
M5
M6
Mg
M9
MIO
MM
Weighted attribute score
Set 2
Average
.17
.21
.36
.35
.52
.45
.19
.44
Rank
order
10
.40
6
g
Value for money
.35
.37
.36
9
Quality o f brand name
Overall evaluation
.33
.42
.56
.59
.45
.51
4
Dollar metric measure
Purchase intention
.58
.54
.65
Brand-specific coefficient
Index of past purchase
.62
.72
.61
.68
.37
.49
•Signirieant at .01 level.
.58
.65
.43
5
1
3
I
7
COMPARISON OF CONSUMER-BASED MEASURES OF BRAND EQUITY
245
Table 5. MNL regression coefficients of lab choice on all the individual level measureii
Coefficients
Measure
Ml
M2
M3
M4
M5
M6
Mg
M9
MIO
Mil
Percentage of unaided recall
Familiarity index
Weighted attribute score
Value for money
Quality of brand name
Overall evaluation
Dollar metric measure
Purchase intention
Brand-specific coefficient
Index of past purchase
Log likelihood
No coefficients
Model
Rho squared
Set 1
Set 2
-0.45
0,42
0.03
0.30
0,04
0,14
0,04 ••
0,03 ••
1,26 •
-0,01
-0,23
0,24
-0,01
-0,08
0.86*
0,26
0,08*
0,01
0.09
0,05
- 197,09
- 100,23
0,49
- 214,05
-66,67
0,69
Note: Alternative specific constants not reported,
*Significanl at ,01 level,
"Significant at ,05 level.
Measures M8 (dollar metric measure) and MIO (brand specific eoefflcients) perform
best in terms of correlations with lab choiees, both at the brand level as well as the
individual level. The unidimensional purchase intention scale M9 also predicts quite
well at both the levels of analysis, TTius, these three measures may be appropriate for
measuring brand equity from the perspective of predictive validity,
M5 (quality of brand name), M8 (dollar metric measure), M9 (purchase intention), and
MIO (brand specific eoefTicient) are significant in predicting choice at the individual
level, when all the ten measures are used simultaneously.
5. Discussion and conclusions
Since all the measures, except Ml, are convergent, it suggests that they are appropriate
indirect brand equity measures as conceptualized by Aaker (1991) and Keller (1993).
They, and the constructs they measure, are all sources that can lead toward creation of
brand equity.
Which of these measures should be used as indicators of brand equity? Managers are
typically interested in how well market shares can be predicted. Five measures perform
well with respect to this criterion: M5, M7, M8, M9, and MIO. These represent the constructs of perceptions (M5), preference (M7, M8), and intentions (M9, MIO), Thus all
these brand equity constructs may be necessary lo fully explain choice, and any one of
them by itself may not be sufficient. Some ofthe measures cited by Winters (1991) assess
only single constructs, while others have composite constructs. Based on the findings
here, multiple construct measures should do better in terms of predictive validity.
246
MANOJ K. AGARWAL AND VrTHALA R. RAO
Among the five measures, two are single-item simple measures—<iuality of brand name
(M5) and purchase intention (M9)—while the others are measures estimated from data
collected in relatively complex tasks—pairwise comparisons (M7, M8) and conjoint
choice experiment (MIO). This suggests that it may not be necessary to subject respondents to difficult questions in order to obtain accurate measures of brand equity. Simple
appropriately worded single-item scales may do just as well. The preceding finding, when
validated in other settings, can be significant to practice because measures like M8 and
MIO can be difficult to obtain, especially when the number of brands being compared is
large.
Our recommendation would be to choose from MS (overall quality of brand name), M8
(dollar metric measure), M9 (purchase intention), and MIO (brand specific choice coefficient). As collecting data for all of these measures would be difficult, M5, M8, and M9
seem like a good subset as they reflect three different basis of equity and additionally, two
of these measures are single-item scales.
This study has several limitations. Our recall measure M1 asked about recall of candy
bars, while all the brands used here may not have been categorized as candy bars by
respondents (for example, Reese's). Some of our measures confound equity with attributebased measures—for example, M3 and M4. For M3, the multiattribute weighted score, the
importance of the attributes could be product specific. For example, peanuty taste may be
important if the candy has peanuts; otherwise, the attribute may be unimportant. We
assumed it has the same im|x>rtance for all candy bars. Due to the enormous amounts of
datarequired,we studied only one product category. The results may be different for other
products. Our validity criterion is based on a measure of choice in a simulated setting. A
better criterion would be to use actual market shares. Unfortunately, this information was
not available for the student market used in this study.
We conclude with two possible directions for further research. The first involves replicating this study with other product categories. The second is torelatethese indirect measures of brand equity to the firm level measures based on market value and the accounting data. Such research will allow a better understanding of the impact of changes in consumer-based measures on strategic brand decisions.
Acknowledgments
We would like to thank Don Lehmann and two anonymous referees for their comments.
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